An image- and BET-based **Monte**-**Carlo** approach to determine mineral accessible surface areas in sandstones

**Monte**-

**Carlo**algorithm, accounts for mineral surface roughness variations, provided by the BET measurements during the determination of the image-based ASAs. Expand abstract.

**Monte**-

**Carlo**algorithm and BET measurements. This joint method consists of three steps: 1) segmentation of pore/mineral phases, 2) calculation of image-based contour surface areas, and 3) determination of a resolution scaling factor (SF). Superior to conventional segmentation methods, which are based on scanning electron microscopy (SEM) images alone, here, the segmentation threshold is independently constrained by both pore size distribution measurements and rock chemical composition analyses. Most importantly, the introduction of an SF, obtained by probability mapping, using a

**Monte**-

**Carlo**algorithm, accounts for mineral surface roughness variations, provided by the BET measurements during the determination of the image-based ASAs. We apply this joint method to a sandstone specimen and confirm the validity and accuracy of the obtained results with our reactive flow-through experiment, reported in Ma et al., 2019. We conclude that our novel method can effectively downscale the image-based ASAs to the atomic BET resolution with minimum assumptions, providing a valuable tool to improve the calculation of fluid-mineral reactions.

10/10 relevant

EarthArXiv

A Non-Orthogonal Variational Quantum Eigensolver

**Monte**

**Carlo**scheme to estimate the uncertainty in the ground state energy caused by a finite number of measurements of the matrix elements. We explain how this

**Monte**

**Carlo**procedure can be extended to adaptively schedule the required measurements, reducing the number of circuit executions necessary for a given accuracy. We apply these ideas to two model strongly correlated systems, a square configuration of H$_4$ and the the $\pi$-system of Hexatriene (C$_6$H$_8$). We show that it is possible to accurately capture the ground state of both systems using a linear combination of simpler ansatz circuits for which it is impossible individually.

4/10 relevant

arXiv

ELMAG 3.01: A three-dimensional **Monte** **Carlo** simulation of
electromagnetic cascades on the extragalactic background light and in
magnetic fields

**Monte**

**Carlo**program for the simulation of electromagnetic cascades initiated by high-energy photons and electrons interacting with extragalactic background light (EBL), is presented. Expand abstract.

**Monte**

**Carlo**program for the simulation of electromagnetic cascades initiated by high-energy photons and electrons interacting with extragalactic background light (EBL), is presented. Pair production and inverse Compton scattering on EBL photons as well as synchrotron losses are implemented using weighted sampling of the cascade development. New features include, among others, the implementation of turbulent extragalactic magnetic fields and the calculation of three-dimensional electron and positron trajectories, solving the Lorentz equation. As final result of the three-dimensional simulations, the program provides two-dimensional source images as function of the energy and the time delay of secondary cascade particles.

10/10 relevant

arXiv

Collective sampling through a Metropolis-Hastings like method: kinetic theory and numerical experiments

**Monte**

**Carlo**methods, we propose to take advantage of the number of duplicates to increase the efficiency of the naive approach. Expand abstract.

**Monte**

**Carlo**methods, an elementary approach would be to duplicate this algorithm as many times as desired. Following the ideas of Population

**Monte**

**Carlo**methods, we propose to take advantage of the number of duplicates to increase the efficiency of the naive approach. Within this framework, each chain is seen as the evolution of a single particle which interacts with the others. In this article, we propose a simple and efficient interaction mechanism and an analytical framework which ensures that the particles are asymptotically independent and identically distributed according to an arbitrary target law. This approach is also supported by numerical simulations showing better convergence properties compared to the classical Metropolis-Hastings algorithm.

5/10 relevant

arXiv

Generating Data using **Monte** **Carlo** Dropout

**Monte**

**Carlo**Dropout method within Autoencoder (MCD-AE) and Variational Autoencoder (MCD-VAE) as efficient generators of synthetic data sets. Expand abstract.

**Monte**

**Carlo**Dropout method within Autoencoder (MCD-AE) and Variational Autoencoder (MCD-VAE) as efficient generators of synthetic data sets. As the Variational Autoencoder (VAE) is one of the most popular generator techniques, we explore its similarities and differences to the proposed methods. We compare the generated data sets with the original data based on statistical properties, structural similarity, and predictive similarity. The results obtained show a strong similarity between the results of VAE, MCD-VAE and MCD-AE; however, the proposed methods are faster and can generate values similar to specific selected initial instances.

10/10 relevant

arXiv

**Monte** **Carlo** Approximation of Bayes Factors via Mixing with Surrogate
Distributions

**Monte**

**Carlo**algorithms that combines the Multiple-try Metropolis and the directional sampling algorithm, which can be used to estimate the normalizing constant when a surrogate distribution is difficult to come by. We illustrate the proposed methods on several statistical models, including the Log-Gaussian Cox process, the Bayesian Lasso, the logistic regression, the Gaussian mixture model, and the g-prior Bayesian variable selection.

7/10 relevant

arXiv

GRASP: a Bayesian network structure learning method using adaptive sequential **Monte** **Carlo**

**Monte**

**Carlo**(SMC) based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). Expand abstract.

**Monte**

**Carlo**(SMC) based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the diversity of sampled networks which were further improved by a new stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASPs potential in discovering novel biological relationships in integrative genomic studies.

10/10 relevant

bioRxiv

Boltzmann machine learning and regularization methods for inferring evolutionary fields and couplings from a multiple sequence alignment

**Monte**

**Carlo**samples that the fields and couplings can be well recovered by the group $L_1$ and the ModAdam method. However, the distribution of evolutionary energies over natural proteins is shifted towards lower energies from that of

**Monte**

**Carlo**samples, indicating that there may be higher-order interactions to favor natural sequences.

4/10 relevant

arXiv

Augmenting **Monte** **Carlo** Dropout Classification Models with Unsupervised
Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults

**Monte**

**Carlo**dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. Expand abstract.

**Monte**

**Carlo**dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently applied to Fault Detection and Di-agnosis (FDD) applications to improve the classification performance on incipient faults. In this paper, we propose a novel approach of augmenting the classification model with an additional unsupervised learning task. We justify our choice of algorithm design via an information-theoretical analysis. Our experimental results on three datasets from diverse application domains show that the proposed method leads to improved fault detection and diagnosis performance, especially on out-of-distribution examples including both incipient and unknown faults.

10/10 relevant

arXiv

Bayesian elastic Full-Waveform Inversion using Hamiltonian **Monte** **Carlo**

**Monte**

**Carlo**sampling reliably recovers important aspects of the posterior, including means, covariances, skewness, as well as 1-D and 2-D marginals. Expand abstract.

**Monte**

**Carlo**sampling of the posterior distribution, (2) the computation of misﬁt derivatives using adjoint techniques, and (3) a mass matrix tuning of the Hamiltonian

**Monte**

**Carlo**algorithm that accounts for the diﬀerent sensitivities of seismic velocities and density. We apply our method to two synthetic end-member scenarios with diﬀerent dimension D that are particularly relevant in the context of full-waveform inversion: low-dimensional models (D < 100) with potentially large variations in material parameters, and high-dimensional models (D > 300000) describing smaller-scale variations of lower amplitude relative to some background. For both end members, the Hamiltonian

**Monte**

**Carlo**sampling reliably recovers important aspects of the posterior, including means, covariances, skewness, as well as 1-D and 2-D marginals. Depending on the strength of material variations, the posterior can be signiﬁcantly non-Gaussian. This suggests to replace local methods for uncertainty quantiﬁcation based on Gaussian assumptions by proper sampling of the posterior. In addition to P-wave and S-wave velocity, the sampling provides constraints on density structure that are free from subjective regularization artifacts.

10/10 relevant

EarthArXiv